AI builds your scenarios for you

Describe the task — an AI agent builds a chatbot that doesn't hallucinate

Tell the agent what the bot should do — it builds the scenario from blocks and branches itself. The finished bot understands free text and adapts to the person, but acts strictly by your logic.

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Describe the task — the agent builds the scenario for you

No need to draw the graph by hand. Tell the AI agent what the bot should do — it creates the blocks, branches and variables itself. You review the finished scenario and apply it.

Agent
Assembling the scenario
Build a bot that qualifies a lead: ask for their name, budget and niche, then hand off to a manager
Analyzing the task 🔍
Creating scenario blocks 📋
Wiring up variables and links
Done — review and apply ✓
Agent proposal +4 NEW
AskName
User prompt
out name
AskBudget
User prompt
out budget
AskNiche
User prompt
out niche
Handoff
Hand off to manager
in name budget niche

From text to a working bot

A plain-English description turns into a finished scenario — blocks, branches and variables are assembled automatically.

You stay in control

The agent proposes a draft; only you apply it. Nothing changes without your approval.

Edits are text too

“Add a question about the city,” “remove the payment step” — the agent applies changes to the existing scenario.

Two extremes — and no good tool in between

Button-based builders are predictable but dumb. Pure LLM agents are smart but uncontrollable. You need the sweet spot.

⚠ Familiar limitations

  • Button bot: only fixed branches
  • The user typed free text — the bot froze
  • The LLM agent answered something you never intended
  • The user changed the topic — the scenario broke
  • No way to see why the agent made a decision

✨ Desman — the sweet spot

  • The agent understands natural language but acts by the scenario
  • The path of steps adapts to how the conversation goes
  • Every step is a block you defined
  • Context switching — automatic
  • Full trace of every agent decision

A smart chatbot with full scenario control

The power of AI understanding inside a strict visual scenario. The agent does exactly what you defined.

New

The agent assembles the scenario

Describe the task in text — the AI agent creates the blocks, branches and variables itself. You see a finished graph draft and apply it with a single click.

Controllable behavior

The agent doesn't make up answers. Every message, question and action is a specific block in your scenario. AI is used for understanding, not for fantasies.

Free-text understanding

Users write however they want — the agent extracts the data it needs from any message. No buttons, rigid forms, or “I didn't understand you.”

Adaptation without chaos

The user switched topics, corrected data, jumped ahead — the agent adapts automatically but stays inside the scenario.

Full transparency

A trace of every dialogue: what the agent asked, what it understood, where the AI made a decision, which prompt it used. Real-time debugging.

Real conversations don't go in a straight line — and your agent is ready for it

The user changes their mind, corrects themselves, jumps ahead or comes back tomorrow — Desman handles it on its own, without a branch for every case.

Bot · lead qualification
live conversation
Hi! I'll help you find the right fit. What's your name?
James
James, how can I help?
I want to run adsdetected intent → ads
What monthly ad budget are you considering?
$5k
What niche are you in?
Wait, the budget isn't $5k, it's $10kwent back — recalculating
Got it, updated to $10,000. Back to the estimate.
Do you even work with marketplaces?switched topic and came back
Yes, we run Amazon and Shopify. Back to the setup — what's your niche?
Feels a bit priceyhandled the objection → came back
It pays back in about 2 months on average — here's the math. Shall we continue?
Scenario canvas — live
Start
Entry point
outsession
AskName
User prompt
afterStart
outname
AskBudget
Prompt · “ads” branch
whenintent = ads
outbudget
AskNiche
User prompt
waitsbudget
outniche
CheckCRM
REST call
afterAskNiche
inname
HandoffSalesGOAL
Hand off to sales
waitsniche
AskTopic
Prompt · “support” branch
whenintent = help
HandoffSupportGOAL
Hand off to support
waitstopic
DetectDoubtRX
Reactive branch
whendoubt
SendROI
Message · estimate
afterDetectDoubt

The customer corrects, interrupts, hesitates — a real conversation doesn't go in a straight line. A button bot would break at any step. Desman handles all four — on its own, without a branch for every case.

Topic switch

The agent detects a new intent, reroutes the path and continues. Data collected earlier is preserved.

Customer: “Hold on, I don't need a repair — I want a warranty return”
The agent switches to the return branch — name and contact are already filled in

Data correction

Updates the variable and automatically recalculates everything that depends on it. No need to start over.

Customer: “Oops, not 10 Oak St — it's 5 Maple Ave”
The agent updates the address, recalculates shipping, continues

Early information

If the user volunteers data the agent hasn't asked for yet — it's saved, and the extra questions are skipped.

Customer: “Hi, I'm James, 555-0199, I'd like to ask about a mortgage”
The agent extracted name + phone + intent — skips 3 questions, straight to the point

Returning later

The customer came back a day or a week later — the bot remembers the conversation and continues from the right spot instead of starting over.

Customer: “Hi, I'm back about my order”
The agent restores context: name and address are already known, continues checkout

How to build an AI chatbot in Desman

From idea to a working agent — no code. The AI assembles the scenario; you just review it.

1

Describe the task to the agent

Tell it in words what the bot should do: who to ask, what to collect, where to hand off.

2

The agent assembles the scenario

The AI creates the blocks, branches and variables. You can also build it by hand in the graph editor.

3

Review and apply

Look over the finished graph, edit it with your mouse or text — and apply with a single click.

4

Connect a channel

Add your bot token — and within 30 seconds it's already running the dialogue from your scenario.

5

Watch and improve

Full real-time trace. See every decision the agent makes.

Desman is built for people already building agents

If you've hit the ceiling of your current tool — Desman fills those gaps.

You build agents for clients

You build custom bots for support, sales, HR. Clients expect a “smart” agent, not a button menu. Desman gives you the tool without months of development — and the first scenario is assembled by the agent itself.

Lead qualification, first-line support, data collection

You automate processes

Integrations are set up, but the conversational part is weak: the bot can't handle off-script answers. Desman drives the dialogue to a result without escalation.

Internal requests, onboarding, inbound inquiries

You're disappointed in pure LLMs

You wired up GPT directly — the agent makes things up and promises the impossible. Desman gives you AI capabilities inside a strict boundary: understanding without hallucinations.

Controlled AI automation

You need results fast

No months of development: describe the task to the agent, review the graph, connect a channel — and the bot is already talking to customers.

Launch in an evening, no developers

Describe the task — and AI builds your first bot

Sign up and tell the agent what the bot should do. It assembles the scenario itself — you review and apply.

Build a bot

No commitment · Questions? Message us on Telegram

Frequently asked questions

Yes. You describe the task in plain text — the agent creates the scenario: question blocks, branches, variables and the links between them. You see a finished draft as a graph and apply it with a single click. The agent only proposes — the final decision is always yours.
Yes. The scenario stays a regular graph in the visual editor — edit it with your mouse, or just ask the agent: “add a question about the city.”
Button-based builders run on a rigid tree — if the user writes off-template, the bot breaks. Desman gives the agent free-text understanding and adaptation, but the agent stays inside your scenario. You control the logic — the AI controls the conversation.
Pure LLM agents are unpredictable — you don't know what they'll say. Desman combines AI understanding with a visual scenario: the agent speaks naturally but follows only the steps you defined. Every decision is visible in the trace.
You define the scenario — the logic the bot follows in the dialogue. From there it doesn't drag the customer down a rigid script: it understands natural language and adjusts the order of steps on the fly. The customer answered several questions at once, jumped ahead, changed their mind, corrected something — the bot reroutes the path within your scenario and reaches the goal. You describe what to collect and do — the bot decides how to run the conversation.
No. Scenarios are assembled in a visual graph editor: blocks, arrows, settings — and the first draft can be assembled by the agent itself. For advanced tasks there are scripts and API calls, but for most scenarios no code is needed.
Connect a channel and the bot starts handling messages right away — the dialogue runs from your scenario within 30 seconds of setup.